Abstract
Social robots keep proliferating. A critical challenge remains their sensible interaction with humans, especially in real world applications. Hence, computing with real world semantics is instrumental. Recently, the Lattice Computing (LC) paradigm has been proposed with a capacity to compute with semantics represented by partial order in a mathematical lattice data domain. In the aforementioned context, this work proposes a parametric LC classifier, namely a Granule-based-Classifier (GbC), applicable in a mathematical lattice (T,⊑) of tree data structures, each of which represents a human face. A tree data structure here emerges from 68 facial landmarks (points) computed in a data preprocessing step by the OpenFace software. The proposed (tree) representation retains human anonymity during data processing. Extensive computational experiments regarding three different pattern recognition problems, namely (1) head orientation, (2) facial expressions, and (3) human face recognition, demonstrate GbC capacities, including good classification results, and a common human face representation in different pattern recognition problems, as well as data induced granular rules in (T,⊑) that allow for (a) explainable decision-making, (b) tunable generalization enabled also by formal logic/reasoning techniques, and (c) an inherent capacity for modular data fusion extensions. The potential of the proposed techniques is discussed.
Highlights
Advances in enabling technologies, both software and hardware, have encouraged a widespread proliferation of social robots in several application domains, including education, therapy, services, entertainment, and arts [1,2,3,4,5]
It turns out that during social robot–human interaction, the robot needs to keep (1) quantifying the engagement/attention of the human it interacts with, (2) modifying its behavior according to a human’s emotions—the latter is directly associated with facial expressions, and (3) personally addressing the human it interacts with
This section deals with three discrete pattern recognition problems, in a unifying manner, in the sense that the same representation of a human face is used in all three problems
Summary
Both software and hardware, have encouraged a widespread proliferation of social robots in several application domains, including education, therapy, services, entertainment, and arts [1,2,3,4,5]. Previous LC classifiers have engaged non-numerical data including, lattice-ordered gender symbols, and events in a probability space, as well as structured data, namely graphs. The latter (graphs) have been used as instruments for ad hoc feature extraction of vectors [25]. The novelties of this work include, first, a unifying, anonymous representation of a human face for face recognition; second, the introduction of the Granule-based-Classifier (GbC) parametric model that processes trees data structures; third, the induction of granular rules, involving tree data structures, toward an explainable artificial intelligence (AI); and fourth, the far-reaching potential of pursuing creativeness by machines based on a lattice order isomorphism.
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